With the increasing proliferation of imaging capabilities, information transactions in computer systems increasingly require the identification and comparison of digital images. In addition to, conventional viewable digital images, other types of information, both viewable and non-viewable, are subject to pattern analysis and matching. Image identification and pattern analysis/recognition is usually dependent on analysis and classification of predetermined features of the image. Accurately identifying images using a computer system is complicated by relatively minor data distorting the images or patterns resulting from changes caused when, for example, are shifted, rotated or otherwise deformed.
Object invariance is a field of visual analysis which deals with recognizing an object despite distortion such as that caused by shifting, rotation, other affine distortions, cropping, etc. Object invariance is used primarily in visual comparison tasks. Identification of a single object or image within a group of objects or images also complicates the image identification process. Selective attention, or “priming”, deals with how a visual object can be separated from its background or other visual objects comprising distractions.
Current pattern recognition, image analysis and information mapping systems typically employ a Bayesian Logic. Bayesian Logic predicts future events through the use of knowledge derived from prior events. In computer applications, Bayesian Logic relies on prior events to formulate or adjust a mathematical model used to calculate the probability of a specific event in the future. Without prior events on which to base a mathematical model, Bayesian Logic is unable to calculate the probability of a future event. Conversely, as the number of prior events increases, the accuracy of the mathematical model increases as does the accuracy of the resulting prediction from the Bayesian Logic approach.
Currently, two common paradigms accommodating some degree of distortion (i.e., image deformation) of a visual object under deformations; point-to-point mapping and high order statistics. Point-to-point, or matching with shape contexts, achieves measurement stability by identifying one or more sub-patterns with the overall patterns or images being compared. Once these sub-patterns are identified, the statistical features of sub-patterns are compared to determine agreement between the two images. Point-to-point mapping methodologies are further described in “Matching with Shape Contexts” by Serge Belongie and Jitendra Malik in June, 2000 during the IEEE Workshop On Content-based Access of Image and Video Libraries (CBAIVL). A second method of point-to-point mapping is what/where networks and assessments of lie groups of transformations based on back propagation networks. In this method an optimal transformation is identified for a feature in a first image and is used to compare the same feature in a second image. This approach deconstructs the image into a sum or a multiplicity of functions. These functions are then mapped to an appropriately deconstructed image function of a compared, or second image. What/where networks have been used by Dr. Rajesh Rao and Dana Ballard from the Salk Institute in La Hoya, Calif. The point-to-point mapping techniques described attempt to map a test or input image to a reference or target image that is either stored in memory directly or is encoded into memory. The point-to-point approach achieves limited image segmentation and mappings through the use of a statistical approach.
In the high order statistical approach both the original input image and the compare target image are mapped into a high dimensional space and statistical measurements are performed on the images in the high dimensional space. These high order statistical measurements are compared to quantity an amount of agreement between the two images indicative of image similarity. This approach is used by Support Vector Machines, High Order Clustering (Hava Siegelmann and Hod Lipson) and Tangent Distance Neural Networks (TDNN). Support Vector Machines are described by Nello Christianini and John Shawe-Taylor ISBN 0-521-78019-5.
Both the point-to-point mapping and the high order statistics approach have been used in an attempt to recognize images subject to various transformations due to shifting, rotation and other deformations of the subject. These approaches are virtually ineffective for effectively isolating a comparison object (selective attention) from the background or other visual objects.
In contrast to these two common paradigms, the human brain may compare two objects or two patterns using “insight” without the benefit of prior knowledge of the objects or the patterns. A Gestalt approach to comparing objects or comparing patterns attempts to include the concept of insight by focusing on the whole object rather than individual portions of the object. Gestalt techniques have not been applied to computer systems to perform pattern recognition, image analysis or information mapping. Gestalt mapping is further described in Vision Science-Photons to Phenomenology by Stephen E. Plamer, ISBN 0-262-16183-4.